The purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets. Fluid identification using our pipeline was tested on ...

Deep learning shows a lot of promise in healthcare, especially in medical imaging, where it can help improve the speed and accuracy of diagnosing patient conditions. But it also faces a serious ...

Self-supervised models generate implicit labels from unstructured data rather than relying on labeled datasets for supervisory signals. Self-supervised learning (SSL), a transformative subset of ...

As impressively capable as AI systems are these days, teaching machines to perform various tasks, whether its translating speech in real time or accurately differentiating between chihuahuas and ...

Use modern machine learning tools and python libraries. Explain how to deal with linearly-inseparable data. Compare logistic regression’s strengths and weaknesses. Explain what decision tree is & how ...

Introduction to Machine Learning: Supervised Learning offers a clear, practical introduction to how machines learn from labeled data to make predictions and decisions. You’ll build a strong foundation ...

What Is Semi-Supervised Learning? Semi-supervised learning is a powerful machine learning technique that combines the strengths of supervised and unsupervised learning. It leverages a small amount of ...

In his 2017 Amazon shareholder letter, Jeff Bezos wrote something interesting about Alexa, Amazon’s voice-driven intelligent assistant: In the U.S., U.K., and Germany, we’ve improved Alexa’s spoken ...

Semi-supervised learning merges supervised and unsupervised methods, enhancing data analysis. This approach uses less labeled data, making it cost-effective yet precise in pattern recognition.